Cloudera

OCTOBER 13, 2020

Data Warehouse – in addition to a number of performance optimizations, DW has added a number of new features for better scalability, monitoring and reliability to enable self-service access with security and performance . Enable Cloud Everywhere : Accelerate adoption of cloud-native data services for public clouds .

Data Warehouse 92

Data Warehouse Machine Learning Visualization Data Lake 92

Octopai

OCTOBER 6, 2022

While the financial stress of migration is usually shouldered by company executives, the entire data team shares the stress of packing up data assets, moving them, unpacking them and putting them in the right locations, and hoping everything got there in one piece, with nothing broken or lost. Test the process. Scrap them.

Testing 52

Testing Visualization Data Transformation Strategy 52

AWS Big Data

FEBRUARY 26, 2024

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. Additionally, you can configure OpenSearch Ingestion to apply data transformations before delivery.

Dashboards 67

Dashboards Testing Metrics Optimization 67

Cloudera

FEBRUARY 8, 2021

Production ML Toolkit – Deploying, Serving, Monitoring, and Governance of ML models. Simple, drag-and-drop building of dashboards and apps with Cloudera Data Visualization. Stock Data – for pulling the stock data, I used alpha vantage service (free version). Now, let’s start testing our model!

Machine Learning 72

Machine Learning Statistics Modeling Dashboards 72

AWS Big Data

MARCH 13, 2024

You can modify the Lambda function to fetch additional vehicle information from a separate data store (for example, a DynamoDB table or a Customer Relationship Management system) to enrich the data, before storing the results in an S3 bucket. In this model, the Lambda function is invoked for each incoming event.

Analytics 103

Analytics IoT Metadata Internet of Things 103

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 75

Sales Visualization Software Marketing 75

DataKitchen

JULY 27, 2023

Azure Databricks, a big data analytics platform built on Apache Spark, performs the actual data transformations. The cleaned and transformed data can then be stored in Azure Blob Storage or moved to Azure Synapse Analytics for further analysis and reporting. Some tools are excellent for batch processing (e.g.,

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

O'Reilly on Data

JANUARY 17, 2023

Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? Building Models. A common task for a data scientist is to build a predictive model. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

AWS Big Data

NOVEMBER 15, 2023

Once released, consumers use datasets from different providers for analysis, machine learning (ML) workloads, and visualization. Each CDH dataset has three processing layers: source (raw data), prepared (transformed data in Parquet), and semantic (combined datasets).

Dashboards 101

Dashboards Analytics Metadata Data Warehouse 101

AWS Big Data

MAY 9, 2023

AWS Glue Studio is a graphical interface that makes it easy to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. It allows you to visually compose data transformation workflows using nodes that represent different data handling steps, which later are converted automatically into code to run.

Visualization 80

Visualization Marketing Big Data IT 80

DataKitchen

OCTOBER 25, 2022

The goal of DataOps Observability is to provide visibility of every journey that data takes from source to customer value across every tool, environment, data store, data and analytic team, and customer so that problems are detected, localized and raised immediately. That data then fills several database tables.

Testing 130

Testing Data-driven Visualization Dashboards 130

AWS Big Data

OCTOBER 11, 2023

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

Management 112

Management Metadata Analytics Dashboards 112

Alation

JANUARY 17, 2023

Great data science tools will assist data scientists and citizen data scientists in testing and training datasets for developing models, and ultimately for deploying them. An example of a data science tool is Dataiku. Examples of data transformation tools include dbt and dataform.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

IBM Big Data Hub

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise. IBM watsonx.ai

Machine Learning 66

Machine Learning Data Warehouse Modeling Cost-Benefit 66

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: How do you use these models?

Metrics 52

Metrics Dashboards Sales Reporting 52

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. Tricentis instills that confidence by providing software tools that enable Agile Continuous Testing (ACT) at scale.

Software 52

Software Data Lake Testing Cost-Benefit 52

Domino Data Lab

AUGUST 20, 2019

Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models. It is a framework for approaching ML as well as providing techniques for extracting features from raw data that can be used within the models. Feature Engineering Terminology and Motivation.

Testing 68

Testing Modeling Interactive Measurement 68

Cloudera

OCTOBER 14, 2020

Also, such a concept helps admin to visualize the jobs which are scheduled for debugging purposes. The Test and Development queue have fixed resource limits. Setting up the cluster to organization hierarchy based resource allocation model. For instance, Spark driver pods need to be scheduled earlier than worker pods.

Machine Learning 114

Machine Learning Management Big Data Optimization 114

Occam's Razor

OCTOBER 19, 2010

If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. If you have evolved to a stage that you need behavior targeting then get Omniture Test and Target or Sitespect. Experimentation and Testing Tools [The "Why" – Part 1]. and embrace Multiplicity.

Analytics 135

Analytics Testing Measurement Optimization 135

FineReport

MARCH 31, 2024

Harnessing the power of advanced APIs, automation, and AI, these tools simplify data compilation, organization, and visualization, empowering users to extract actionable insights effortlessly. Key features include comprehensive data connectors, user-friendly report-building tools, and web-based sharing options.

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

AWS Big Data

DECEMBER 21, 2023

For the downstream consumption by all departments across the organization, smava’s Data Platform team prepares curated data products following the extract, load, and transform (ELT) pattern. The Raw Vault describes objects loaded directly from the data sources and represents a copy of the landing stage in the data lake.

Data Lake 97

Data Lake Data Warehouse Data-driven B2B 97

AWS Big Data

APRIL 5, 2023

In this post, we share how Showpad used QuickSight to streamline data and insights access across teams and customers. Showpad migrated over 70 dashboards with over 1,000 visuals. The serverless model was also compelling because we did not have to pay for server instances nor license fees per reader.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52